基于ROS系统的智能车自主路径规划与避障控制

基于ROS系统的智能车自主路径规划与避障控制，可以使用ROS中的Navigation Stack和Move Base包来实现。具体步骤如下：

1. 确定传感器：智能车需要安装激光雷达、摄像头等传感器，用于感知周围环境。

2. 创建地图：使用激光雷达等传感器采集地图数据，并使用SLAM算法生成地图。

3. 路径规划：使用Navigation Stack中的global planner进行全局路径规划，使用local planner进行局部路径规划。

4. 避障控制：使用Move Base包中的costmap进行障碍物检测与避障控制。

5. 控制执行：将路径规划和避障控制的结果转换为机器人的控制指令，控制车辆执行动作。

在以上步骤中，用户需要根据具体场景进行参数配置，并根据实际情况进行调试和优化。

相关问题

基于ROS系统的智能车自主路径规划与避障控制的代码

这里提供一个基于ROS系统的智能车自主路径规划与避障控制的demo代码，供参考：

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import rospy
import tf
from nav_msgs.msg import Odometry
from geometry_msgs.msg import Twist, PoseStamped
from move_base_msgs.msg import MoveBaseActionResult
from sensor_msgs.msg import LaserScan

class RobotControl():
    def __init__(self):
        rospy.init_node('robot_control', anonymous=False)

        # 激光雷达
        self.laser_sub = rospy.Subscriber('/scan', LaserScan, self.laser_callback)

        # 移动底盘
        self.cmd_vel_pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)

        # 导航目标点
        self.goal_sub = rospy.Subscriber('/move_base_simple/goal', PoseStamped, self.goal_callback)
        self.goal_result_sub = rospy.Subscriber('/move_base/result', MoveBaseActionResult, self.goal_result_callback)

        # 机器人位姿
        self.odom_sub = rospy.Subscriber('/odom', Odometry, self.odom_callback)

        # 初始化
        self.goal = None
        self.goal_reached = False
        self.robot_pose = None
        self.laser_data = None

    def laser_callback(self, data):
        self.laser_data = data

    def goal_callback(self, data):
        self.goal = data
        self.goal_reached = False

    def goal_result_callback(self, data):
        if data.status.status == 3:
            self.goal_reached = True

    def odom_callback(self, data):
        self.robot_pose = data.pose.pose

    def get_distance(self, goal_pose):
        return ((goal_pose.position.x - self.robot_pose.position.x) ** 2 + (goal_pose.position.y - self.robot_pose.position.y) ** 2) ** 0.5

    def get_angle(self, goal_pose):
        quaternion = (self.robot_pose.orientation.x, self.robot_pose.orientation.y, self.robot_pose.orientation.z, self.robot_pose.orientation.w)
        euler = tf.transformations.euler_from_quaternion(quaternion)
        yaw = euler[2]

        goal_quaternion = (goal_pose.orientation.x, goal_pose.orientation.y, goal_pose.orientation.z, goal_pose.orientation.w)
        goal_euler = tf.transformations.euler_from_quaternion(goal_quaternion)
        goal_yaw = goal_euler[2]

        return goal_yaw - yaw

    def rotate(self, angle):
        cmd_vel = Twist()
        cmd_vel.angular.z = 0.3 if angle > 0 else -0.3
        while abs(angle) > 0.1:
            self.cmd_vel_pub.publish(cmd_vel)
            angle = self.get_angle(self.goal.pose)
            rospy.sleep(0.1)
        cmd_vel.angular.z = 0
        self.cmd_vel_pub.publish(cmd_vel)

    def move(self):
        cmd_vel = Twist()
        distance = self.get_distance(self.goal.pose)
        while distance > 0.1:
            if self.laser_data is not None:
                min_range = min(self.laser_data.ranges)
                if min_range < 0.5:
                    cmd_vel.linear.x = 0
                    self.cmd_vel_pub.publish(cmd_vel)
                    return
            cmd_vel.linear.x = 0.3
            self.cmd_vel_pub.publish(cmd_vel)
            distance = self.get_distance(self.goal.pose)
            rospy.sleep(0.1)
        cmd_vel.linear.x = 0
        self.cmd_vel_pub.publish(cmd_vel)

    def run(self):
        rate = rospy.Rate(10)
        while not rospy.is_shutdown():
            if self.goal is not None and not self.goal_reached:
                angle = self.get_angle(self.goal.pose)
                if abs(angle) > 0.1:
                    self.rotate(angle)
                else:
                    self.move()
                    if self.goal_reached:
                        rospy.loginfo('Goal reached!')
            rate.sleep()

if __name__ == '__main__':
    robot_control = RobotControl()
    robot_control.run()

在以上代码中，我们使用了激光雷达和机器人位姿等传感器信息，实现了机器人的自主路径规划和避障控制。具体实现过程中，我们订阅了激光雷达数据、导航目标点、机器人位姿和导航结果等消息，根据这些信息计算机器人的控制指令，从而实现路径规划和避障控制的功能。

基于 ROS 系统的智能清洁车自主路径规划与避障控制的代码

以下是一个基于ROS系统的智能清洁车自主路径规划与避障控制的代码示例，具体实现可能因应用场景不同而有所差异：

#include <ros/ros.h>
#include <sensor_msgs/LaserScan.h>
#include <geometry_msgs/Twist.h>

class Robot
{
public:
    Robot(){
        sub = n.subscribe("/scan", 1, &Robot::callback, this);
        pub = n.advertise<geometry_msgs::Twist>("/cmd_vel", 1);
        linear_vel = 0.2;
        angular_vel = 1.0;
    }

    void callback(const sensor_msgs::LaserScan::ConstPtr& msg){
        float min_angle = msg->angle_min;
        float max_angle = msg->angle_max;
        float angle_increment = msg->angle_increment;
        float range_min = msg->range_min;
        float range_max = msg->range_max;
        std::vector<float> ranges = msg->ranges;

        float front_obstacle_distance = std::numeric_limits<float>::infinity();
        float front_obstacle_angle = 0.0;

        for(int i=0; i<ranges.size(); i++){
            float angle = min_angle + i*angle_increment;
            float range = ranges[i];

            if(range < range_max && range > range_min){
                if(angle <= 0.1745 && angle >= -0.1745){ // front
                    if(range < front_obstacle_distance){
                        front_obstacle_distance = range;
                        front_obstacle_angle = angle;
                    }
                }
            }
        }

        geometry_msgs::Twist msg_twist;
        if(front_obstacle_distance < 1.0){
            msg_twist.linear.x = 0.0;
            msg_twist.angular.z = angular_vel;
        }
        else if(front_obstacle_distance < 2.0){
            msg_twist.linear.x = 0.5*linear_vel;
            msg_twist.angular.z = 0.5*angular_vel*front_obstacle_angle;
        }
        else{
            msg_twist.linear.x = linear_vel;
            msg_twist.angular.z = 0.0;
        }

        pub.publish(msg_twist);
    }

private:
    ros::NodeHandle n;
    ros::Subscriber sub;
    ros::Publisher pub;
    float linear_vel, angular_vel;
};

int main(int argc, char** argv){
    ros::init(argc, argv, "robot");
    Robot robot;
    ros::spin();
    return 0;
}

上述代码中，Robot类订阅了激光雷达的数据，并根据激光雷达数据计算出前方障碍物距离和角度。根据前方障碍物的距离和角度，决定机器人的运动方向（前进、左转、右转），并通过发布Twist消息控制机器人运动。